Meteorix commented on a change in pull request #7146: URL: https://github.com/apache/tvm/pull/7146#discussion_r547091707
########## File path: python/tvm/topi/cuda/batch_matmul_tensorcore.py ########## @@ -0,0 +1,274 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=invalid-name,too-many-locals,unused-variable,unused-argument +"""cuda batch_matmul operators""" +import tvm +from tvm import autotvm +from tvm import te +from ..utils import traverse_inline, get_const_tuple +from .tensor_intrin import intrin_wmma_load_matrix_A, \ + intrin_wmma_load_matrix_W, intrin_wmma_store_matrix, intrin_wmma_gemm + [email protected]_topi_compute("batch_matmul_tensorcore.cuda") +def batch_matmul_tensorcore(cfg, x, y, out_shape=None): + """batch matmul tensorcore operator on cuda""" + # todo: deal with out_shape for broadcast, liuxin.ai + return batch_matmul_tensorcore_cuda(x, y) + + [email protected]_topi_schedule("batch_matmul_tensorcore.cuda") +def schedule_batch_matmul_tensorcore(cfg, outs): + """Schedule for batch_matmul operator using Tensorcore + + Parameters + ---------- + outs: Array of Tensor + The computation graph description of batch_matmul + in the format of an array of tensors. + + Returns + ------- + s: Schedule + The computation schedule for the op. + """ + outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs + s = te.create_schedule([x.op for x in outs]) + + def _schedule(cfg, s, C): + A, B = s[C].op.input_tensors + batch, m_dim, k_dim = get_const_tuple(A.shape) + batch, n_dim, k_dim = get_const_tuple(B.shape) + out_dtype = C.dtype + # inline astype fp16 + s[A].compute_inline() + s[B].compute_inline() + + # Explicit memory access + AS = s.cache_read(A, 'shared', [C]) + BS = s.cache_read(B, 'shared', [C]) + AF = s.cache_read(AS, 'wmma.matrix_a', [C]) + BF = s.cache_read(BS, 'wmma.matrix_b', [C]) + CF = s.cache_write(C, 'wmma.accumulator') + CS = s.cache_read(CF, 'shared', [C]) + + # fallback support + target = tvm.target.Target.current() + if cfg.is_fallback: + ref_log = autotvm.tophub.load_reference_log( + target.kind.name, target.model, 'batch_matmul_tensorcore.cuda') + cfg.fallback_with_reference_log(ref_log) + + # Deal with op fusion, such as bias/relu and slice after padding + if C.op not in s.outputs and "injective" in s.outputs[0].tag: + s[C].compute_inline() + C = s.outputs[0].output(0) + + # create tuning space + cfg.define_knob("block_row_warps", [1, 2, 4]) + cfg.define_knob("block_col_warps", [1, 2, 4]) + cfg.define_knob("warp_row_tiles", [1, 2, 4]) + cfg.define_knob("warp_col_tiles", [1, 2, 4]) + cfg.define_knob("chunk", [1, 2, 4, 8]) + cfg.define_knob("offset", [0, 8]) + cfg.define_knob("offsetCS", [0, 8]) + cfg.define_knob("vec", [1, 2, 4, 8]) + + # Ensure that the default parameters are applicable when autotvm is not in use + if (m_dim % 32 == 0 and n_dim % 8 == 0): + cfg.define_knob("wmma_m", [32, 16, 8]) + elif (m_dim % 16 == 0 and n_dim % 16 == 0): + cfg.define_knob("wmma_m", [16, 8, 32]) + elif (m_dim % 8 == 0 and n_dim % 32 == 0): + cfg.define_knob("wmma_m", [8, 16, 32]) + + warp_size = 32 + wmma_k = 16 + block_row_warps = cfg["block_row_warps"].val + block_col_warps = cfg["block_col_warps"].val + warp_row_tiles = cfg["warp_row_tiles"].val + warp_col_tiles = cfg["warp_col_tiles"].val + chunk = cfg["chunk"].val + offset = cfg["offset"].val + offsetCS = cfg["offsetCS"].val + wmma_m = cfg["wmma_m"].val + vec = cfg["vec"].val + + if wmma_m == 16: + wmma_n = 16 + elif wmma_m == 8: + wmma_n = 32 + elif wmma_m == 32: + wmma_n = 8 + + # Define the stride of intrin functions + AS_align = chunk * wmma_k + offset + BS_align = chunk * wmma_k + offset + CS_align = warp_col_tiles * block_col_warps * wmma_n + offsetCS + AS_stride = [AS_align, 1] + BS_stride = [BS_align, 1] + AF_stride = [wmma_k, 1] + BF_stride = [wmma_k, 1] + CF_stride = [warp_col_tiles * wmma_n, 1] + CS_stride = [CS_align, 1] + + block_x = te.thread_axis('blockIdx.x') + block_y = te.thread_axis('blockIdx.y') + block_z = te.thread_axis('blockIdx.z') + thread_x = te.thread_axis('threadIdx.x') + thread_y = te.thread_axis('threadIdx.y') + thread_z = te.thread_axis('threadIdx.z') + + # Schedule for dense computation + block_factor_m = wmma_m * warp_row_tiles * block_row_warps + block_factor_n = wmma_n * warp_col_tiles * block_col_warps + b, m, n = C.op.axis + block_i, bc = s[C].split(m, factor=block_factor_m) + block_j, oc = s[C].split(n, factor=block_factor_n) + s[C].reorder(b, block_i, block_j, bc, oc) + t = s[C].fuse(bc, oc) + t, vi = s[C].split(t, factor=vec) + t, tx = s[C].split(t, factor=warp_size) + t, ty = s[C].split(t, factor=block_row_warps) + t, tz = s[C].split(t, factor=block_col_warps) + s[C].bind(block_i, block_x) + s[C].bind(block_j, block_y) + s[C].bind(b, block_z) + s[C].bind(tz, thread_z) + s[C].bind(ty, thread_y) + s[C].bind(tx, thread_x) + s[C].vectorize(vi) + + # Schedule for wmma store + s[CS].compute_at(s[C], block_j) + bs, bb, oo = CS.op.axis + s[CS].storage_align(bb, CS_align - 1, CS_align) + bb, bbi = s[CS].split(bb, factor=wmma_m) + oo, ooi = s[CS].split(oo, factor=wmma_n) + bb, bbii = s[CS].split(bb, factor=warp_row_tiles) + oo, ooii = s[CS].split(oo, factor=warp_col_tiles) + s[CS].reorder(bs, bb, oo, bbii, ooii, bbi, ooi) + + # Schedule for wmma computation + s[CF].compute_at(s[CS], oo) + bs, warp_i, warp_j = CF.op.axis + warp_i, _ii = s[CF].split(warp_i, factor=wmma_m) + warp_j, _jj = s[CF].split(warp_j, factor=wmma_n) + k, = CF.op.reduce_axis + k, _k = s[CF].split(k, factor=wmma_k) + ko, ki = s[CF].split(k, factor=chunk) + s[CF].reorder(bs, ko, ki, warp_i, warp_j, _ii, _jj, _k) + + # Schedule for wmma_matrix_a load + s[AF].compute_at(s[CF], ki) + bs, b, i = AF.op.axis + b, b_ii = s[AF].split(b, factor=wmma_m) + i, i_jj = s[AF].split(i, factor=wmma_k) + s[AF].reorder(bs, b, i, b_ii, i_jj) + + # Schedule for wmma_matrix_b load + s[BF].compute_at(s[CF], ki) + bs, o, i = BF.op.axis + o, o_ii = s[BF].split(o, factor=wmma_n) + i, i_ii = s[BF].split(i, factor=wmma_k) + s[BF].reorder(bs, o, i, o_ii, i_ii) + + # Schedule for A's(B's) shared memory load + def shared_shedule(stage, strides): + s[stage].compute_at(s[CF], ko) + bs, xo, yo = stage.op.axis + s[stage].storage_align(xo, strides - 1, strides) + t = s[stage].fuse(xo, yo) + t, vi = s[stage].split(t, factor=vec) + t, tx = s[stage].split(t, factor=warp_size) + t, ty = s[stage].split(t, factor=block_row_warps) + _, tz = s[stage].split(t, factor=block_col_warps) + s[stage].bind(ty, thread_y) + s[stage].bind(tz, thread_z) + s[stage].bind(tx, thread_x) + s[stage].vectorize(vi) + + shared_shedule(AS, AS_align) + shared_shedule(BS, BS_align) + + shape = (wmma_m, wmma_n, wmma_k) + in_dtype = 'float16' Review comment: I just kept the same with code for dense_tensorcore https://github.com/apache/tvm/blob/main/python/tvm/relay/op/strategy/cuda.py#L679 ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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